Comparison of Computer Vision and Photogrammetric Approaches for Epipolar Resampling of Image Sequence.
Comparison of Computer Vision and Photogrammetric Approaches for Epipolar Resampling of Image Sequence.
复制标题
DOI：
10.3390/s16030412
复制
发表时间：
2016-03-22
期刊：
影响因子：
--
通讯作者：
Kim T
中科院分区：
文献类型：
--
作者：
Kim JI;Kim T
对极重采样是消除立体图像之间垂直视差的过程。由于其重要性，计算机视觉和摄影测量领域已经开发了许多方法。然而，我们认为图像序列的极线重采样（而不是单个对）尚未得到彻底研究。在本文中，我们比较了两个领域中开发的用于处理图像序列的极线重采样方法。首先，我们简要回顾了计算机视觉和摄影测量极线重采样方法中发展的未校准和校准极线重采样方法。众所周知，计算机视觉和摄影测量中开发的极线重采样方法在数学上是相同的，但我们还指出了它们之间参数估计的差异。其次，我们测试了这两个领域的代表性重采样方法并进行了分析。我们表明，对于单个图像对的极线重采样，可以使用所有未校准和测试的摄影测量方法。更重要的是，我们还表明，对于图像序列，除了摄影测量贝叶斯方法之外，所有测试的方法都显示极线重采样性能存在显着变化。我们的结果表明贝叶斯方法有利于图像序列的极线重采样。
Epipolar resampling is the procedure of eliminating vertical disparity between stereo images. Due to its importance, many methods have been developed in the computer vision and photogrammetry field. However, we argue that epipolar resampling of image sequences, instead of a single pair, has not been studied thoroughly. In this paper, we compare epipolar resampling methods developed in both fields for handling image sequences. Firstly we briefly review the uncalibrated and calibrated epipolar resampling methods developed in computer vision and photogrammetric epipolar resampling methods. While it is well known that epipolar resampling methods developed in computer vision and in photogrammetry are mathematically identical, we also point out differences in parameter estimation between them. Secondly, we tested representative resampling methods in both fields and performed an analysis. We showed that for epipolar resampling of a single image pair all uncalibrated and photogrammetric methods tested could be used. More importantly, we also showed that, for image sequences, all methods tested, except the photogrammetric Bayesian method, showed significant variations in epipolar resampling performance. Our results indicate that the Bayesian method is favorable for epipolar resampling of image sequences.